10 papers
Decentralized Linearized Consensus ADMM with Efficient Quantized Communication
Boyu Han, Xu Du, Karl H. Johansson +1
Distributed optimization offers significant advantages over centralized methods in terms of scalability and robustness when solving large-scale problems. In this paper, we propose…
CADMM-Prox: A Bi-level Consensus ADMM for Non-smooth Non-convex Distributed Consensus Optimization
Xu Du, Shuting Wu, Karl H. Johansson +1
Non-smooth and non-convex optimization problems are pervasive in machine learning, control, and signal processing, due to the need for sparse solutions and the inherently non-conve…
Nesterov Accelerated Distributed Optimization with Efficient Quantized Communication
Ruochen Wu, Xu Du, Karl H. Johansson +1
In modern large-scale networked systems, rapidly solving optimization problems while utilizing communication resources efficiently is critical for addressing complex tasks. In this…
Lightweight Real-Time ALADIN for Distributed Optimization
Yifei Wang, Xuhui Feng, Shimin Pan +3
This paper presents a real-time computational framework for multi-node distributed optimization by extending the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN)…
Mix-CALADIN: A Distributed Algorithm for Consensus Mixed-Integer Optimization
Boyu Han, Xu Du, Karl H. Johansson +1
This paper addresses distributed consensus optimization problems with mixed-integer variables, with a specific focus on Boolean variables. We introduce a novel distributed algorith…
Affine-coupled Distributed Optimization via Distributed Proximal Jacobian ADMM with Quantized Communication
Xu Du, Boyu Han, Ivano Notarnicola +2
This paper investigates distributed resource allocation optimization over directed graphs with limited communication bandwidth. We develop a novel distributed algorithm that integr…